mlsanityData to 14 Oct 2026

Techniques for Interpretable Machine Learning

arXiv 1808.00033 ↗First posted 31 Jul 2018cs.LG cs.AI stat.MLcited by 238

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Abstract (arXiv)

Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision. Although many approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. We provide a survey covering existing techniques to increase the interpretability of machine learning models. We also discuss crucial issues that the community should consider in future work such as designing user-friendly explanations and developing comprehensive evaluation metrics to further push forward the area of interpretable machine learning.

Key facts. Cited by 238 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Jul 2018.

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Citing papers per year: 2021: 47; 2022: 47; 2023: 64; 2024: 44; 2025: 27; 2026: 9.

238 papers.

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  1. 2023Explainability for Large Language Models: A Surveycited by 333
  2. 2022Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanismcited by 171
  3. 2022Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanationscited by 159
  4. 2021Post-hoc Interpretability for Neural NLP: A Surveycited by 156
  5. 2021Trustworthy AI: A Computational Perspectivecited by 93
  6. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 91
  7. 2021Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Modelscited by 78
  8. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 76
  9. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  10. 2023Applying Large Language Models and Chain-of-Thought for Automatic Scoringcited by 69
  11. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  12. 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needscited by 64
38 more
  1. 2021Fairness via Representation Neutralizationcited by 63
  2. 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 53
  3. 2021S-LIME: Stabilized-LIME for Model Explanationcited by 52
  4. 2024Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Eracited by 46
  5. 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
  6. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  7. 2023Disentanglement via Latent Quantizationcited by 40
  8. 2024Trust in AI: Progress, Challenges, and Future Directionscited by 35
  9. 2022The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizationscited by 29
  10. 2022Scalable Interpretability via Polynomialscited by 25
  11. 2022SEAT: Stable and Explainable Attentioncited by 25
  12. 2022Explainable AI for clinical and remote health applications: a survey on tabular and time series datacited by 24
  13. 2021Explainable Tsetlin Machine framework for fake news detection with credibility score assessmentcited by 23
  14. 2024Local vs. Global Interpretability: A Computational Complexity Perspectivecited by 23
  15. 2021An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinerycited by 22
  16. 2021The Impact of Machine Learning on 2D/3D Registration for Image-guided Interventions: A Systematic Review and Perspectivecited by 22
  17. 2023Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneckcited by 22
  18. 2023Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanationscited by 22
  19. 2021Self-Interpretable Model with TransformationEquivariant Interpretationcited by 21
  20. 2022Mind the gap: Challenges of deep learning approaches to Theory of Mindcited by 21
  21. 2023Model Based Explanations of Concept Driftcited by 21
  22. 2023CoRTX: Contrastive Framework for Real-time Explanationcited by 20
  23. 2022Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Versioncited by 19
  24. 2024FaithLM: Towards Faithful Explanations for Large Language Modelscited by 19
  25. 2021Deep Learning for Android Malware Defenses: a Systematic Literature Reviewcited by 18
  26. 2022A Survey on Aspect-Based Sentiment Classificationcited by 18
  27. 2022FR: Folded Rationalization with a Unified Encodercited by 18
  28. 2023DEGREE: Decomposition Based Explanation For Graph Neural Networkscited by 18
  29. 2021Modeling Techniques for Machine Learning Fairness: A Surveycited by 17
  30. 2023The role of causality in explainable artificial intelligencecited by 17
  31. 2021SurvNAM: The machine learning survival model explanationcited by 16
  32. 2022Attribution-based XAI Methods in Computer Vision: A Reviewcited by 16
  33. 2022Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challengescited by 15
  34. 2023Interpretation of Time-Series Deep Models: A Surveycited by 15
  35. 2021Model-Based Counterfactual Synthesizer for Interpretationcited by 14
  36. 2022DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systemscited by 14
  37. 2023Efficient XAI Techniques: A Taxonomic Surveycited by 14
  38. 2021Applying Machine Learning to Study Fluid Mechanicscited by 13

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